Rational Basis Functions in Iterative Learning Control for Multivariable Systems

Feedforward control with task flexibility for MIMO systems is essential to meet ever-increasing demands on throughput and accuracy. The aim of this paper is to develop a framework for data-driven tuning of rational feedforward controllers in iterative learning control (ILC) for noncommutative MIMO systems. A convex optimization problem in ILC...

Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder

Mechatronic systems have increasingly stringent performance requirements for motion control, leading to a situation where many factors, such as position-dependency, cannot be neglected in feedforward control. The aim of this paper is to compensate for position-dependent effects by modeling feedforward parameters as a function of position. A...

Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and...

Position-Dependent Snap Feedforward: A Gaussian Process Framework

Mechatronic systems have increasingly high performance requirements for motion control. The low-frequency contribution of the flexible dynamics, i.e., the compliance, should be compensated for by means of snap feedforward to achieve high accuracy. Position-dependent compliance, which often occurs in motion systems, requires the snap feedforward...

Gaussian Processes for Advanced Motion Control

Machine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and...

Longitudinal analysis of diffusion-weighted MRI with a ball-and-sticks model

Purpose: To increase the sensitivity in longitudinal analysis of DW-MRI data with the ball-and-sticks model.

Total Correlation-Based Groupwise Image Registration for Quantitative MRI

In quantitative magnetic resonance imaging (qMRI), quantitative tissue properties can be estimated by fitting a signal model to the voxel intensities of a series of images acquired with different settings. To obtain reliable quantitative measures, it is necessary that the qMRI images are spatially aligned so that a given voxel corresponds in...

Vergelijking van 2 wrijvingsreductie systemen in de triaxiaalproef (NEN 12697-25B) op een asfalt proefstuk inclusief rekstrooksignalen

Een van de functionele proeven uit de Type Testing voor de CE-markering is de triaxiaalproef (NEN 12697-25B), die wordt gebruikt voor de bepaling van de vervormingsgevoeligheid van het asfaltmengsel en uitgevoerd wordt op in het laboratorium vervaardigde cilindrische proefstukken. Afhankelijk van de maximale korrelgrootte in het asfaltproefstuk...

Modeling the brain morphology distribution in the general aging population

Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the...

Meso Mechanical Analysis of AC Mixture Response

Ongoing research into performance modeling of Asphalt Concrete (AC) mixtures using meso mechanics approaches is being undertaken at Delft University of Technology (TUD). The approach has already been successfully employed for evaluating the long term performance of porous asphalt concrete. The work presented in this paper extends the use for...

Creating transport models that matter: A strategic view on governance of transport models and road maps for innovation

The basic use of transport models is that they help to increase our understanding of the mobility system. In more operational terms, they can be a basis for developing quantitative insights in the past and current state of the system or scenarios about the future. Models are also useful as impact assessment tools in decision making processes or...

Robust estimation of the noise variance from background MR data

In the literature, many methods are available for estimation of the variance of the noise in magnetic resonance (MR) images. A commonly used method, based on the maximum of the background mode of the histogram, is revisited and a new, robust, and easy to use method is presented based on maximum likelihood (ML) estimation. Both methods are...